OpenAI's ChatGP has attracted worldwide attention, which has led to the rapid popularization of the concept of artificial intelligence generative content (AIGC). "ChatGPT is popular, will AIGC replace the search engine?" 》。 This application of generative AI is not only changing the way individuals access information, but also gradually changing all aspects of business management.
With the popularization of artificial intelligence technology, some enterprises have begun to use this technology to develop management aids for specific industries or fields to help managers break cognitive boundaries and build a generative management decision-making model that integrates human experience and artificial intelligence. For example, in corporate strategic management, the traditional decision-making process is often based on the personal cognition of senior managers, forming a top-down empirical management model. However, with the complexity of the business environment and the deepening of industrial transformation, the development direction of enterprises has developed from single to multi-direction, which requires managers to be able to deal with more complex internal and external factors.
In this context, business leaders are beginning to recognize the value of technology in digital transformation. Especially in the post-pandemic era, most companies have accelerated the pace of transformation. Standing at the beginning of the next decade, the era of comprehensive corporate reinvention has arrived. Emerging technologies such as artificial intelligence will be a key force driving the development of this era.
The development and application of generative artificial intelligence will make enterprise management more intelligent and efficient. Dr. Luo Rentong, an expert in the application of digital economy, believes that by combining human experience and artificial intelligence technology, enterprises can better cope with the complex and changeable business environment, so as to achieve faster and more accurate decision-making. At the same time, it will also provide more innovation and development opportunities for enterprises, and promote enterprises to gain advantages in the new competitive environment.
Fruit in high places
With this round of technology pictures, the boom of large language models (LLMs) and foundational models is driving the development of generative artificial intelligence, which is an important turning point in the field of artificial intelligence. Generative AI is an AI neural network based on a large amount of data and built through preset and self-correcting feedback mechanisms that can generate larger scale data. It can understand the elements of human creation, recombine those elements, and create new content that humans can understand.
Not only do these techniques decipher the complexity of language, enabling machines to learn context, infer intent, and create independently, but they can also quickly adapt to a variety of different tasks. This will have a profound impact on the future of work, business models and social life, greatly enhancing human creativity and productivity.
Currently, most enterprises choose to purchase Model-as-a-Service directly for business applications. However, for many businesses, the greatest value may come from customizing or fine-tuning models with their own data to meet their unique needs.
For example, businesses can easily acquire and use generative AI and large language model applications. They can call these programs through APIs and use hint engineering techniques such as prompt learning and prefix learning, to customize them to their specific needs. How can enterprises achieve strategic layout in this wave of AI and artificial intelligence? 》。
In order to increase the value of generative AI and basic models in specific business applications, enterprises will increasingly use their own data to fine-tune pre-trained models to achieve customization, so as to achieve qualitative improvement. From generating possible outcomes to judging and cutting the likelihood of outcomes, generative AI is gradually transforming output from uncertainty to certainty.
For example, online travel agencies deploy ChatGPT plugins as "intelligent customer service" to provide users with personalized travel advice, helping users determine travel arrangements faster. For unexpected itinerary changes, travelers can quickly resolve issues through intelligent customer service that is online around the clock. In addition, generative AI can also be applied to content creation, code writing, process automation, information security, and more.
Think carefully and act with dedication
More and more Chinese companies are actively exploring generative AI technology and are beginning to apply large-scale language models to achieve more innovation and efficiency improvement. Enterprises need to see a clear direction, but also carefully explore in the process, such as selecting some low-risk areas for feasibility assessment, and then carrying out generative AI pilots to explore innovation potential.
The reason why the popularization of artificial intelligence has just begun is because the huge algorithm, computing power and data gap between artificial intelligence theory and practice has only recently been broken. The stock price of NVIDIA, which played a key role in this breakthrough, has soared in recent times, and it is still difficult to find a card worldwide. Many startups and even Internet giants have expressed their desire for All-in generative artificial intelligence, but few can really do it. It also reflects the difference between knowledge and action, as well as the discontinuity between corporate planning and execution.
There are many paths from the present to the future, which path to choose is determined by "knowing", and the improvement of "knowledge" from uncertainty to certainty can help people choose the best path they think, but whether the path in reality is feasible requires overcoming discontinuities in the future path.
Under the condition of high uncertainty in the future development direction and high discontinuity of the future development path, the development of things is in a chaotic state, and most enterprises adopt adaptive management, exclude in many development directions, try to find the development direction suitable for their own resources and ability conditions, and bet limited resources and capabilities on this development direction, and strive to achieve a breakthrough in the development path and enter the growth stage.
Enterprises in the growth stage often adopt vision-based management, and there are always enterprises that can overcome the discontinuity in the development path, realize their own vision, enter the stage of low uncertainty and discontinuity of the development direction, and begin to adopt a planned management model, try their best to stabilize their development direction and make their development path continue. However, when companies face drastic changes, they have to make new choices to adapt to the external environment with increased uncertainty about the direction of development.
According to Dr. Luo Rentong, an expert in digital economy application practice, enterprises should pay attention to the integration of knowledge and action when exploring generative artificial intelligence technology. This means that companies need to combine theoretical knowledge with practical application to ensure the effective application of innovation and technology. At the same time, companies should be cautious when choosing a development direction and reduce risks through feasibility assessments and pilot projects. In addition, companies need to be aware of discontinuities in their future growth paths and be flexible in the face of drastic changes.
Generative management
In the era of information explosion and convenient Internet, planned management is easy for enterprises to ignore external signals, resulting in organizational rigidity and missed development opportunities.
Too many market signals can also lead to emergent management firms wandering and expanding randomly in the market, even though they see multiple paths but cannot be consistent. Therefore, enterprises that lack the ability to process massive amounts of information at high speed cannot create optimal performance, no matter which of adaptive, visionary, planned, or emergent management is adopted. Faced with a complex external environment and multiple business combinations, enterprises can learn from the logic of generative AI technology and try generative AI management logic.
In the divergent stage, the generative artificial intelligence management mechanism obtains massive information from each business and pre-trains the enterprise decision-making model, so that it can learn the laws and patterns in the data, and then predict the subsequent business direction and future situation of the enterprise.
In the convergence phase, the trained model enters the decision aid simulation stage, and the generated data is provided for the manager's reference. At the same time, the manager evaluates the decision scheme of the model output to facilitate subsequent improvement of the model and improve the quality of the generated results.
In practice, the bottom-up and top-down decision-making methods adopted by enterprises have a generative management model.
For example, when discussing the transformation of Huawei's decision-making system, Ren Zhengfei said: "I don't know how many resources are suitable for the front line, so I can only let the people who can hear the gunshots call for artillery fire, because he is the closest to the customer, everyone listens to him first, and chooses to believe him first." When we reviewed it afterwards, we found that we had wasted ammunition, and then we 'settled the accounts after the autumn' and summed up the experience. "Every call for artillery fire on Huawei's front line is a bottom-up strategic output. Why is it inevitable that Huawei will be sanctioned? How should enterprises respond to the changes in the world pattern accelerating the digital economy? 》
With the continuous development of enterprises and the rapid change of the external environment, many enterprises have a combination of innovative, growing, mature and transformational businesses at the same point in time. These businesses face different development directions, uncertainties and discontinuities in development paths.
In order to cope with the opportunities and challenges faced by various businesses, enterprises will serve different types of users, adopt corresponding organizational structures, develop products that meet user needs, and try to compete and cooperate in different market environments. As a result, the complexity of corporate management began to increase exponentially.
Generative management includes two core concepts, one is "birth" and the other is "success". The basis of the overall management objectives of the enterprise is the management objectives of each business, and the management objectives of each business include user management, organization management, product management and market management.
In terms of "life", enterprises can use pre-trained management models to label the management problems and data information faced by each type of business, and continuously optimize the model through the evaluation of managers to form a bottom-up management information collection system.
In terms of "success", it includes the achievement of various business goals and the achievement of the company's overall goals. Since the management objectives of each business include specific goals such as user management, organization management, product management and market management. Taken together, these objectives should achieve the overall management objectives of each business, and the convergence of the overall objectives of the business at different stages of development should be able to achieve the overall goals at the company level.
Top management can evaluate and rank the achievement of business goals from top to bottom, and adjust the company-level return model to help each business achieve its own goals while helping the company achieve its overall management goals.
Business management is a science that can deal with uncertainty in management through the formulation of rules. Use the certainty of rules to deal with the uncertainty of change, and the continuity of processes to deal with the discontinuity of behavior. Managers need to intervene appropriately in the management system, including but not limited to the evaluation of management data, feedback on management behavior, and optimization of management models.
Dr. Luo Rentong, an expert in digital economy application practice, believes that building generative management is not a temporary response or remedy, but should be comprehensively planned from the early design stage and continuously optimized throughout the life cycle.
The key to generative management lies in the interaction of decision-making models and assignment strategies, self-optimization, and the ability to autonomously generate a large amount of content and quickly screen to find the best solution when receiving external instructions.
The development of generative artificial intelligence has not only brought challenges, but also mental developments.
Related industrial changes:
How can AI empower manufacturing? What are the application scenarios of the new AIGC trend in the manufacturing industry? 》
"Hollywood collective strike to protest AI for jobs, generative artificial intelligence AIGC will have any impact on the film and television industry? 》
What opportunities and challenges will AI pharma bring to the industry? 》
How can the construction industry go digital? 》
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